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Efficient estimation of a disease prevalence using auxiliary ranks information

Author

Listed:
  • Ehsan Zamanzade

    (University of Isfahan
    Institute for Research in Fundamental Sciences (IPM))

  • Hadi Saboori

    (University of Zabol)

  • Hani M. Samawi

    (Georgia Southern University)

Abstract

It is a common challenge in medical field to obtain the prevalence of a specific disease within a given population. To tackle this problem, researchers usually draw a random sample from the target population to obtain an accurate estimate of the proportion of diseased people. However, some limitations may occur in practice due to constraints, such as complexity or cost. In these situations, some alternative sampling techniques are needed to achieve precision with smaller sample sizes. One such approach is Neoteric Ranked Set Sampling (NRSS), which is a variation of Ranked Set Sampling (RSS) design. NRSS scheme involves selecting sample units using a rank-based method that incorporates auxiliary information to obtain a more informative sample. In this article, we focus on the problem of estimating the population proportion using NRSS. We develop an estimator for the population proportion using the NRSS design and establish some of its properties. We employ Monte Carlo simulations to compare the proposed estimator with competitors in Simple Random Sampling (SRS) and RSS designs. Our results demonstrate that statistical inference based on the introduced estimator can be significantly more efficient than its competitors in RSS and SRS designs. Finally, to demonstrate the effectiveness of the proposed procedure in estimating breast cancer prevalence within the target population, we apply it to analyze Wisconsin Breast Cancer data.

Suggested Citation

  • Ehsan Zamanzade & Hadi Saboori & Hani M. Samawi, 2025. "Efficient estimation of a disease prevalence using auxiliary ranks information," Computational Statistics, Springer, vol. 40(5), pages 2729-2748, June.
    • Handle: RePEc:spr:compst:v:40:y:2025:i:5:d:10.1007_s00180-024-01580-4
    DOI: 10.1007/s00180-024-01580-4
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    References listed on IDEAS

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    1. Cesar Augusto Taconeli & Suely Ruiz Giolo, 2020. "Maximum likelihood estimation based on ranked set sampling designs for two extensions of the Lindley distribution with uncensored and right-censored data," Computational Statistics, Springer, vol. 35(4), pages 1827-1851, December.
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    9. M. Mahdizadeh & Ehsan Zamanzade, 2021. "Smooth estimation of the area under the ROC curve in multistage ranked set sampling," Statistical Papers, Springer, vol. 62(4), pages 1753-1776, August.
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    13. Jesse Frey, 2012. "Constrained nonparametric estimation of the mean and the CDF using ranked-set sampling with a covariate," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 64(2), pages 439-456, April.
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